Interval-Partitioned and Correlated Uncertainty Set Based Robust Optimization of Microgrid

The dramatic increase in renewable energy sources has created significant uncertainties in the operation of power systems. This article investigates a day-ahead economic dispatch problem for a typical microgrid, considering the uncertainties of renewable energy sources and load demand. An interval-p...

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Bibliographic Details
Published in:IEEE systems journal 2024-09, Vol.18 (3), p.1516-1527
Main Authors: Zheng, Zuqing, Chen, Guo, Shen, Zixiang
Format: Article
Language:English
Subjects:
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Alternative energy sources
Costs
Dispatching
Distributed generation
Energy resources
interval-partitioned
Load modeling
Microgrids
Optimization
Optimization models
outer approximation
Power dispatch
Renewable energy sources
Renewable resources
Robustness (mathematics)
temporal-correlated
two-stage robust optimization
Uncertainty
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Summary:The dramatic increase in renewable energy sources has created significant uncertainties in the operation of power systems. This article investigates a day-ahead economic dispatch problem for a typical microgrid, considering the uncertainties of renewable energy sources and load demand. An interval-partitioned and temporal-correlated uncertainty set based robust optimization model is proposed, which allows a more accurate characterization of the distribution of uncertainties. The proposed robust optimization model can reduce the conservativeness of the optimal solution by avoiding scenarios that are low-probability or even impossible in reality. The model is then decomposed into a master problem and a nonlinear bi-level subproblem and solved by the C \& CG method and Big-M method. However, this method requires the introduction of a large number of auxiliary variables and related constraints, significantly increasing the computation burden. To tackle this problem, an efficient solution method, Improved-C \& CG, is developed by integrating an outer approximation method into the C \& CG method. Finally, case studies verify the effectiveness of the proposed model, uncertainty set, and solution methods.
ISSN:1932-8184
1937-9234
DOI:10.1109/JSYST.2024.3406698